Large Language Model Prompt Datasets: An In-depth Analysis and Insights
✨步子哥 (steper)
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2025年12月11日 03:49
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Large Language Model Prompt Datasets: An In-depth Analysis and Insights
Beijing Jiaotong University, Aalborg University, Bowling Green State University
October 10, 2025
Abstract
A prompt is a natural language instruction that defines a specific task for a large language model (LLM) and serves as the primary interface for human-LLM interaction. With the growing deployment of LLMs, diverse prompt datasets are emerging from platforms such as GitHub and social media. These datasets span a wide array of applications and content types, facilitating both broader LLM utilization and improved prompt engineering.
Data Collection
Comprehensive collection of 1.22 TB of data, comprising 673M+ prompt instances from 129 heterogeneous sources:
Dataset Platforms
Academic Publications
Public Repositories
Social Media
Taxonomy
Hierarchical categorization of LLM prompt datasets by:
Downstream Tasks
Languages
Engineering Techniques
Attributes
Modalities
Analysis Methodology
Multi-level linguistic analysis across three dimensions on seven representative datasets:
Lexical
Token distribution, vocabulary analysis
Syntactic
Dependency parsing, POS tagging, TF-IDF
Semantic
Topic modeling, semantic similarity
Key Findings
Prompts exhibit distinct compositional patterns compared to other text corpora
Domain-specific variations in prompt construction across different applications
Unique linguistic properties distinguish prompts from literature and web content
Prompts tend to be more directive and task-oriented than general text
Optimization Approach
Novel prompt optimization method leveraging syntactic embeddings:
Extract POS & Dependency Features
→
Identify Centroid Representation
→
Guide LLMs to Rewrite Prompts
Results in improved meaningfulness and quality of model outputs.
Impact and Applications
First comprehensive compilation of prompt datasets
Provides foundation for systematic prompt engineering research
Enables more effective prompt selection and refinement
Facilitates broader LLM deployment across diverse applications
Resources
Datasets and code available for research use:
https://anonymous.4open.science/r/LLM-Prompt-Datasets-7416Over 1.22 TB of curated prompt data for research use
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